
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import plotly.express as px
import emoji
import string
import nltk
from PIL import Image
from collections import Counter
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.stem.snowball import SnowballStemmer
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC,LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.pipeline import Pipeline
import pickle


data=pd.read_csv('Hotel_Reviews.csv')


data.head()


data.tail()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 515738 entries, 0 to 515737
Data columns (total 17 columns):
 #   Column                                      Non-Null Count   Dtype  
---  ------                                      --------------   -----  
 0   Hotel_Address                               515738 non-null  object 
 1   Additional_Number_of_Scoring                515738 non-null  int64  
 2   Review_Date                                 515738 non-null  object 
 3   Average_Score                               515738 non-null  float64
 4   Hotel_Name                                  515738 non-null  object 
 5   Reviewer_Nationality                        515738 non-null  object 
 6   Negative_Review                             515738 non-null  object 
 7   Review_Total_Negative_Word_Counts           515738 non-null  int64  
 8   Total_Number_of_Reviews                     515738 non-null  int64  
 9   Positive_Review                             515738 non-null  object 
 10  Review_Total_Positive_Word_Counts           515738 non-null  int64  
 11  Total_Number_of_Reviews_Reviewer_Has_Given  515738 non-null  int64  
 12  Reviewer_Score                              515738 non-null  float64
 13  Tags                                        515738 non-null  object 
 14  days_since_review                           515738 non-null  object 
 15  lat                                         512470 non-null  float64
 16  lng                                         512470 non-null  float64
dtypes: float64(4), int64(5), object(8)
memory usage: 66.9+ MB


data.isnull().sum()

Hotel_Address                                    0
Additional_Number_of_Scoring                     0
Review_Date                                      0
Average_Score                                    0
Hotel_Name                                       0
Reviewer_Nationality                             0
Negative_Review                                  0
Review_Total_Negative_Word_Counts                0
Total_Number_of_Reviews                          0
Positive_Review                                  0
Review_Total_Positive_Word_Counts                0
Total_Number_of_Reviews_Reviewer_Has_Given       0
Reviewer_Score                                   0
Tags                                             0
days_since_review                                0
lat                                           3268
lng                                           3268
dtype: int64


data.dropna(inplace=True,axis=0)


data.isnull().sum()

Hotel_Address                                 0
Additional_Number_of_Scoring                  0
Review_Date                                   0
Average_Score                                 0
Hotel_Name                                    0
Reviewer_Nationality                          0
Negative_Review                               0
Review_Total_Negative_Word_Counts             0
Total_Number_of_Reviews                       0
Positive_Review                               0
Review_Total_Positive_Word_Counts             0
Total_Number_of_Reviews_Reviewer_Has_Given    0
Reviewer_Score                                0
Tags                                          0
days_since_review                             0
lat                                           0
lng                                           0
dtype: int64


data['Negative_Review'].value_counts()

No Negative                                                                    127035
 Nothing                                                                        14227
 Nothing                                                                         4212
 nothing                                                                         2211
 N A                                                                             1032
                                                                                ...  
 Room wasn t ready rooms freezing hotel basic and outdated                          1
 not so close to underground                                                        1
 There was a terrible smell when you switched on the light in the bathroom          1
 A bit far with underground walk more than 5 minutes                                1
 I was in 3rd floor It didn t work Free Wife                                        1
Name: Negative_Review, Length: 327927, dtype: int64


data.describe()


data.describe(include='object').T


data["Hotel_Address"].head(10)

0     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
1     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
2     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
3     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
4     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
5     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
6     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
7     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
8     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
9     s Gravesandestraat 55 Oost 1092 AA Amsterdam ...
Name: Hotel_Address, dtype: object


print("Duplicated rows before: ",data.duplicated().sum())
data.drop_duplicates(inplace=True)
print("Duplicated rows after: ",data.duplicated().sum())

Duplicated rows before:  526
Duplicated rows after:  0


data["Hotel_Address"]=data["Hotel_Address"].str.replace("United Kingdom","UK")


data.head()


data[data['Average_Score']==9.8].Hotel_Name

54717    Ritz Paris
54718    Ritz Paris
54719    Ritz Paris
54720    Ritz Paris
54721    Ritz Paris
54722    Ritz Paris
54723    Ritz Paris
54724    Ritz Paris
54725    Ritz Paris
54726    Ritz Paris
54727    Ritz Paris
54728    Ritz Paris
54729    Ritz Paris
54730    Ritz Paris
54731    Ritz Paris
54732    Ritz Paris
54733    Ritz Paris
54734    Ritz Paris
54735    Ritz Paris
54736    Ritz Paris
54737    Ritz Paris
54738    Ritz Paris
54739    Ritz Paris
54740    Ritz Paris
54741    Ritz Paris
54742    Ritz Paris
54743    Ritz Paris
54744    Ritz Paris
Name: Hotel_Name, dtype: object


plt.figure(figsize=(15,10))
sns.heatmap(data=data.corr(),annot=True)

<AxesSubplot:>


sns.countplot(data=data[data['Reviewer_Score']==10],x=data[data['Reviewer_Score']==10].Reviewer_Nationality.head(200))
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34]),
 [Text(0, 0, ' United Kingdom '),
  Text(1, 0, ' Italy '),
  Text(2, 0, ' Netherlands '),
  Text(3, 0, ' United States of America '),
  Text(4, 0, ' Ireland '),
  Text(5, 0, ' Australia '),
  Text(6, 0, ' Canada '),
  Text(7, 0, ' Argentina '),
  Text(8, 0, ' France '),
  Text(9, 0, ' Russia '),
  Text(10, 0, ' Croatia '),
  Text(11, 0, ' United Arab Emirates '),
  Text(12, 0, ' Panama '),
  Text(13, 0, ' New Zealand '),
  Text(14, 0, ' Norway '),
  Text(15, 0, ' India '),
  Text(16, 0, ' Israel '),
  Text(17, 0, ' Isle of Man '),
  Text(18, 0, ' Liechtenstein '),
  Text(19, 0, ' United States Minor Outlying Islands '),
  Text(20, 0, ' Morocco '),
  Text(21, 0, ' Oman '),
  Text(22, 0, ' Germany '),
  Text(23, 0, ' Belgium '),
  Text(24, 0, ' Spain '),
  Text(25, 0, ' China '),
  Text(26, 0, ' Greece '),
  Text(27, 0, ' Sweden '),
  Text(28, 0, ' Taiwan '),
  Text(29, 0, ' Lebanon '),
  Text(30, 0, ' Thailand '),
  Text(31, 0, ' Japan '),
  Text(32, 0, ' Turkey '),
  Text(33, 0, ' Saudi Arabia '),
  Text(34, 0, ' Slovakia ')])


sns.countplot(data=data[data['Reviewer_Score']==2.5],x=data[data['Reviewer_Score']==2.5].Reviewer_Nationality.head(200))
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37]),
 [Text(0, 0, ' United Kingdom '),
  Text(1, 0, ' Saudi Arabia '),
  Text(2, 0, ' France '),
  Text(3, 0, ' United States of America '),
  Text(4, 0, ' Netherlands '),
  Text(5, 0, ' South Africa '),
  Text(6, 0, ' Ireland '),
  Text(7, 0, ' Malaysia '),
  Text(8, 0, ' Philippines '),
  Text(9, 0, ' Fiji '),
  Text(10, 0, ' United Arab Emirates '),
  Text(11, 0, ' Turkey '),
  Text(12, 0, ' Germany '),
  Text(13, 0, ' Egypt '),
  Text(14, 0, ' Bahrain '),
  Text(15, 0, ' Romania '),
  Text(16, 0, ' Portugal '),
  Text(17, 0, ' Japan '),
  Text(18, 0, ' Qatar '),
  Text(19, 0, ' Belarus '),
  Text(20, 0, ' Spain '),
  Text(21, 0, ' Lithuania '),
  Text(22, 0, ' Lebanon '),
  Text(23, 0, ' Russia '),
  Text(24, 0, ' Hong Kong '),
  Text(25, 0, ' Namibia '),
  Text(26, 0, ' Greece '),
  Text(27, 0, ' Kuwait '),
  Text(28, 0, ' Vietnam '),
  Text(29, 0, ' Australia '),
  Text(30, 0, ' Italy '),
  Text(31, 0, ' China '),
  Text(32, 0, ' Brazil '),
  Text(33, 0, ' Ukraine '),
  Text(34, 0, ' Belgium '),
  Text(35, 0, ' '),
  Text(36, 0, ' Nigeria '),
  Text(37, 0, ' Indonesia ')])


data['Review_Date']=pd.to_datetime(data['Review_Date'])
data['years']=data['Review_Date'].dt.year
data['months']=data['Review_Date'].dt.month


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 511944 entries, 0 to 515737
Data columns (total 19 columns):
 #   Column                                      Non-Null Count   Dtype         
---  ------                                      --------------   -----         
 0   Hotel_Address                               511944 non-null  object        
 1   Additional_Number_of_Scoring                511944 non-null  int64         
 2   Review_Date                                 511944 non-null  datetime64[ns]
 3   Average_Score                               511944 non-null  float64       
 4   Hotel_Name                                  511944 non-null  object        
 5   Reviewer_Nationality                        511944 non-null  object        
 6   Negative_Review                             511944 non-null  object        
 7   Review_Total_Negative_Word_Counts           511944 non-null  int64         
 8   Total_Number_of_Reviews                     511944 non-null  int64         
 9   Positive_Review                             511944 non-null  object        
 10  Review_Total_Positive_Word_Counts           511944 non-null  int64         
 11  Total_Number_of_Reviews_Reviewer_Has_Given  511944 non-null  int64         
 12  Reviewer_Score                              511944 non-null  float64       
 13  Tags                                        511944 non-null  object        
 14  days_since_review                           511944 non-null  object        
 15  lat                                         511944 non-null  float64       
 16  lng                                         511944 non-null  float64       
 17  years                                       511944 non-null  int64         
 18  months                                      511944 non-null  int64         
dtypes: datetime64[ns](1), float64(4), int64(7), object(7)
memory usage: 78.1+ MB


sns.pointplot(data=data,x=data['years'],y=data['Total_Number_of_Reviews'])
plt.xticks(rotation=90)

(array([0, 1, 2]),
 [Text(0, 0, '2015'), Text(1, 0, '2016'), Text(2, 0, '2017')])


sns.lineplot(data=data,x=data['months'],y=data['Total_Number_of_Reviews'])

<AxesSubplot:xlabel='months', ylabel='Total_Number_of_Reviews'>


sns.lineplot(data=data,x=data['months'],y=data['Review_Total_Negative_Word_Counts'])

<AxesSubplot:xlabel='months', ylabel='Review_Total_Negative_Word_Counts'>


sns.lineplot(data=data,x=data['months'],y=data['Review_Total_Positive_Word_Counts'])

<AxesSubplot:xlabel='months', ylabel='Review_Total_Positive_Word_Counts'>


sns.lineplot(data=data,x=data['months'],y=data['Average_Score'])

<AxesSubplot:xlabel='months', ylabel='Average_Score'>


sns.lineplot(data=data,x=data['months'],y=data['Reviewer_Score'])

<AxesSubplot:xlabel='months', ylabel='Reviewer_Score'>


sns.countplot(data=data,x=data['Average_Score'])
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]),
 [Text(0, 0, '5.2'),
  Text(1, 0, '6.4'),
  Text(2, 0, '6.6'),
  Text(3, 0, '6.7'),
  Text(4, 0, '6.8'),
  Text(5, 0, '6.9'),
  Text(6, 0, '7.0'),
  Text(7, 0, '7.1'),
  Text(8, 0, '7.2'),
  Text(9, 0, '7.3'),
  Text(10, 0, '7.4'),
  Text(11, 0, '7.5'),
  Text(12, 0, '7.6'),
  Text(13, 0, '7.7'),
  Text(14, 0, '7.8'),
  Text(15, 0, '7.9'),
  Text(16, 0, '8.0'),
  Text(17, 0, '8.1'),
  Text(18, 0, '8.2'),
  Text(19, 0, '8.3'),
  Text(20, 0, '8.4'),
  Text(21, 0, '8.5'),
  Text(22, 0, '8.6'),
  Text(23, 0, '8.7'),
  Text(24, 0, '8.8'),
  Text(25, 0, '8.9'),
  Text(26, 0, '9.0'),
  Text(27, 0, '9.1'),
  Text(28, 0, '9.2'),
  Text(29, 0, '9.3'),
  Text(30, 0, '9.4'),
  Text(31, 0, '9.5'),
  Text(32, 0, '9.6'),
  Text(33, 0, '9.8')])


import plotly.express as px
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot


data.Reviewer_Nationality.nunique()

227


# Get the top 10 reviewer nationalities with the most reviews
nationality = data["Reviewer_Nationality"].value_counts(dropna=False)[:10]

# Create a bar chart of the nationalities and review counts
fig = px.bar(x=nationality.index, y=nationality.values, color=nationality.index,
             title="Top 10 Nationalities of Reviewers")
fig.update_layout(xaxis_title="Nationality", yaxis_title="Review Count", font=dict(size=14))
fig.show()


data["Hotel_Name"].nunique()

1475


# Get the top 10 hotels with the most reviews
names = data["Hotel_Name"].value_counts(dropna=False)[:10]

fig = px.bar(x=names.index, y=names.values, color=names.index,
             title="Top 10 Hotels with the Most Reviews")
fig.update_layout(xaxis_title="Hotel Name", yaxis_title="Review Count", font=dict(size=14))
fig.show()


fig = px.histogram(data, x="Reviewer_Score", title='Review Score Distribution', nbins=20, text_auto=True)
fig.show()


fig = px.histogram(data, x="Average_Score", title='Review Average Score Distribution')
fig.show()


data['Negative_Review'][1]

'No Negative'


data.loc[:, 'Positive_Review'] = data.Positive_Review.apply(lambda x: x.replace('No Positive', ''))
data.loc[:, 'Negative_Review'] = data.Negative_Review.apply(lambda x: x.replace('No Negative', ''))


data['Negative_Review'][1]

''


data["Total_Review"] = data["Negative_Review"] + data["Positive_Review"]


data["review_type"] = data["Reviewer_Score"].apply(
    lambda x: "Bad_review" if x < 7 else "Good_review")


df_reviews = data[["Total_Review", "review_type"]]


df_reviews


fig = px.histogram(df_reviews, x="review_type", title='Review Type Distribution', text_auto=True)
fig.show()


df_reviews[df_reviews.review_type == 'Good_review'].Total_Review.value_counts()

 Location                                                                      940
 Nothing Everything                                                            936
 Everything                                                                    597
 Great location                                                                252
 Everything                                                                    203
                                                                              ... 
 Outdated hotel rooms a bit shabby arogant receptionists Excellent location      1
 Staff unobtrusive but efficient Queries answered in a helpful manner            1
 Room Service food was awful but breakfast was good                              1
 all good location                                                               1
 I was in 3rd floor It didn t work Free Wife  staff was very kind                1
Name: Total_Review, Length: 411312, dtype: int64


df_reviews[df_reviews.review_type == 'Bad_review'].Total_Review.value_counts()

 Everything Nothing                                                                                    123
 Location                                                                                              105
 Nothing                                                                                                36
 location                                                                                               26
 Staff                                                                                                  22
                                                                                                      ... 
 The hotel is not four star                                                                              1
 The staff checking us in was rude and very un polite The breakfast was cold and tasted disgusting       1
 Noisy fan so couldn t sleep kettle didn t work cold shower                                              1
 Hotel isn t good marked from the street no window not clear bed clothes dirty mirror good terry         1
 The ac was useless It was a hot week in vienna and it only gave more hot air                            1
Name: Total_Review, Length: 85115, dtype: int64


good_reviews = df_reviews[df_reviews.review_type == "Good_review"]
bad_reviews = df_reviews[df_reviews.review_type == "Bad_review"]


good_df = good_reviews.sample(n=len(bad_reviews), random_state=42)

df_review_resampled = good_df.append(bad_reviews).reset_index(drop=True)
df_review_resampled.shape

(172350, 2)


df_review_resampled.head()


df_review_resampled.rename(columns={'Total_Review':'text'}, inplace=True)


sns.countplot(
  x='review_type',
  data=df_review_resampled,
  order=df_review_resampled.review_type.value_counts().index
)

plt.xlabel("type")
plt.title("Review type (resampled)");


def strip_emoji(text):
    return emoji.replace_emoji(text,replace="")


def strip_all_entities(text):
    text = text.replace('\r', '').replace('\n', ' ').lower()
    text = re.sub(r"(?:\@|https?\://)\S+", "", text)
    text = re.sub(r'[^\x00-\x7f]',r'', text)
    text = re.sub(r'(.)1+', r'1', text)
    text = re.sub('[0-9]+', '', text)
    stopchars= string.punctuation
    table = str.maketrans('', '', stopchars)
    text = text.translate(table)
    text = ' '.join(text)
    return text


def decontract(text):
    text = re.sub(r"can\'t", "can not", text)
    text = re.sub(r"n\'t", " not", text)
    text = re.sub(r"\'re", " are", text)
    text = re.sub(r"\'s", " is", text)
    text = re.sub(r"\'d", " would", text)
    text = re.sub(r"\'ll", " will", text)
    text = re.sub(r"\'t", " not", text)
    text = re.sub(r"\'ve", " have", text)
    text = re.sub(r"\'m", " am", text)
    return text


def clean_hashtags(tweet):
    new_tweet = " ".join(word.strip() for word in re.split('#(?!(?:hashtag)\b)[\w-]+(?=(?:\s+#[\w-]+)*\s*$)', tweet))
    new_tweet2 = " ".join(word.strip() for word in re.split('#|_', new_tweet))
    return new_tweet2


def filter_chars(a):
    sent = []
    for word in a.split(' '):
        if ('$' in word) | ('&' in word):
            sent.append('')
        else:
            sent.append(word)
    return ' '.join(sent)


def remove_mult_spaces(text):
    return re.sub("\s\s+"," ",text)


def stemmer(text):
    tokenized = nltk.word_tokenize(text)
    ps = PorterStemmer()
    return ' '.join([ps.stem(words) for words in tokenized])


def lemmatize(text):
    tokenized = nltk.word_tokenize(text)
    lm = WordNetLemmatizer()
    return ' '.join([lm.lemmatize(words) for words in tokenized])


def preprocess(text):
    text = strip_emoji(text)
    text = decontract(text)
   # text = strip_all_entities(text)
    text = clean_hashtags(text)
    text = filter_chars(text)
    text = remove_mult_spaces(text)
    text = stemmer(text)
    text = lemmatize(text)
    return text


import nltk
nltk.download('omw-1.4')

[nltk_data] Downloading package omw-1.4 to
[nltk_data]     C:\Users\Vidit\AppData\Roaming\nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!

True


df_review_resampled.head()


df_review_resampled['review_type'].nunique()

2


review_type=['Good_review','Bad_review']


df_review_resampled['cleaned_text']=df_review_resampled['text'].apply(preprocess)
df_review_resampled.head()


df_review_resampled["cleaned_text"].duplicated().sum()

4604


df_review_resampled.drop_duplicates("cleaned_text", inplace=True)


df_review_resampled['review_list'] = df_review_resampled['cleaned_text'].apply(word_tokenize)
df_review_resampled.head()


text_len = []
for text in df_review_resampled.review_list:
    review_len = len(text)
    text_len.append(review_len)
df_review_resampled['text_len'] = text_len


df_review_resampled=df_review_resampled[df_review_resampled['text_len']!=0]


df_review_resampled.shape

(167745, 5)


from sklearn.preprocessing import LabelEncoder
encoder =LabelEncoder()
encoded_review = encoder.fit_transform(df_review_resampled.review_type.values)


encoded_review[1:5]

array([1, 1, 1, 1])


from sklearn.model_selection import train_test_split, GridSearchCV

X_train, X_test, y_train, y_test = train_test_split(
    df_review_resampled.text, 
    encoded_review, 
    test_size=0.25, 
    random_state=42
  )


X_train.shape, X_test.shape, y_train.shape, y_test.shape

((125808,), (41937,), (125808,), (41937,))


tf_idf = TfidfVectorizer()
X_train_tf = tf_idf.fit_transform(X_train)
X_test_tf = tf_idf.transform(X_test)
print(X_train_tf.shape)
print(X_test_tf.shape)

(125808, 43624)
(41937, 43624)


lr=LogisticRegression()


lr_cv_score=cross_val_score(lr,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)


mean_lr_cv = np.mean(lr_cv_score)
mean_lr_cv

0.8260478114536053


lin_svc = LinearSVC()


lin_svc_cv_score = cross_val_score(lin_svc,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)
mean_lin_svc_cv = np.mean(lin_svc_cv_score)
mean_lin_svc_cv

0.8181824314320018


multiNB = MultinomialNB()
multiNB_cv_score = cross_val_score(multiNB,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)
mean_multiNB_cv = np.mean(multiNB_cv_score)
mean_multiNB_cv

0.7969156929095422


dtree = DecisionTreeClassifier()
dtree_cv_score = cross_val_score(dtree,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)
mean_dtree_cv = np.mean(dtree_cv_score)
mean_dtree_cv

0.709411235999091


rand_forest = RandomForestClassifier()
rand_forest_cv_score = cross_val_score(rand_forest,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)


mean_rand_forest_cv = np.mean(rand_forest_cv_score)
mean_rand_forest_cv

0.7973628754332519


adab=AdaBoostClassifier()


adab_cv_score = cross_val_score(adab,X_train_tf,y_train,cv=5,scoring='f1_macro',n_jobs=-1)
mean_adab_cv = np.mean(adab_cv_score)
mean_adab_cv

0.7739219811500005


svc1 = LinearSVC()
param_grid = {'C':[0.0001,0.001,0.01,0.1,1,10],
'loss':['hinge','squared_hinge'],
'fit_intercept':[True,False]}
grid_search = GridSearchCV(svc1,param_grid,cv=5,scoring='f1_macro',n_jobs=-1,verbose=0,return_train_score=True)
grid_search.fit(X_train_tf,y_train)

GridSearchCV(cv=5, estimator=LinearSVC(), n_jobs=-1,
             param_grid={'C': [0.0001, 0.001, 0.01, 0.1, 1, 10],
                         'fit_intercept': [True, False],
                         'loss': ['hinge', 'squared_hinge']},
             return_train_score=True, scoring='f1_macro')


grid_search.best_estimator_

LinearSVC(C=0.1)


grid_search.best_score_

0.8259393847278773


lin_svc.fit(X_train_tf,y_train)
y_pred = lin_svc.predict(X_test_tf)


def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):
    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)
    fig = plt.figure(figsize=figsize)
    try:
        heatmap = sns.heatmap(df_cm, annot=True, fmt="d")
    except ValueError:
        raise ValueError("Confusion matrix values must be integers.")
    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
    plt.ylabel('Truth')
    plt.xlabel('Prediction')


cm = confusion_matrix(y_test,y_pred)
print_confusion_matrix(cm,review_type)


print('Classification Report:\n',classification_report(y_test, y_pred, target_names=review_type))

Classification Report:
               precision    recall  f1-score   support

 Good_review       0.81      0.83      0.82     21213
  Bad_review       0.83      0.80      0.81     20724

    accuracy                           0.82     41937
   macro avg       0.82      0.82      0.82     41937
weighted avg       0.82      0.82      0.82     41937


pickle.dump(tf_idf, open('hotel_reviews.pkl', 'wb'))
pickle.dump(lin_svc, open('hotelreviews.pkl', 'wb'))

	Hotel_Address	Additional_Number_of_Scoring	Review_Date	Average_Score	Hotel_Name	Reviewer_Nationality	Negative_Review	Review_Total_Negative_Word_Counts	Total_Number_of_Reviews	Positive_Review	Review_Total_Positive_Word_Counts	Total_Number_of_Reviews_Reviewer_Has_Given	Reviewer_Score	Tags	days_since_review	lat	lng
515733	Wurzbachgasse 21 15 Rudolfsheim F nfhaus 1150 ...	168	8/30/2015	8.1	Atlantis Hotel Vienna	Kuwait	no trolly or staff to help you take the lugga...	14	2823	location	2	8	7.0	[' Leisure trip ', ' Family with older childre...	704 day	48.203745	16.335677
515734	Wurzbachgasse 21 15 Rudolfsheim F nfhaus 1150 ...	168	8/22/2015	8.1	Atlantis Hotel Vienna	Estonia	The hotel looks like 3 but surely not 4	11	2823	Breakfast was ok and we got earlier check in	11	12	5.8	[' Leisure trip ', ' Family with young childre...	712 day	48.203745	16.335677
515735	Wurzbachgasse 21 15 Rudolfsheim F nfhaus 1150 ...	168	8/19/2015	8.1	Atlantis Hotel Vienna	Egypt	The ac was useless It was a hot week in vienn...	19	2823	No Positive	0	3	2.5	[' Leisure trip ', ' Family with older childre...	715 day	48.203745	16.335677
515736	Wurzbachgasse 21 15 Rudolfsheim F nfhaus 1150 ...	168	8/17/2015	8.1	Atlantis Hotel Vienna	Mexico	No Negative	0	2823	The rooms are enormous and really comfortable...	25	3	8.8	[' Leisure trip ', ' Group ', ' Standard Tripl...	717 day	48.203745	16.335677
515737	Wurzbachgasse 21 15 Rudolfsheim F nfhaus 1150 ...	168	8/9/2015	8.1	Atlantis Hotel Vienna	Hungary	I was in 3rd floor It didn t work Free Wife	13	2823	staff was very kind	6	1	8.3	[' Leisure trip ', ' Family with young childre...	725 day	48.203745	16.335677

	Additional_Number_of_Scoring	Average_Score	Review_Total_Negative_Word_Counts	Total_Number_of_Reviews	Review_Total_Positive_Word_Counts	Total_Number_of_Reviews_Reviewer_Has_Given	Reviewer_Score	lat	lng
count	512470.000000	512470.000000	512470.000000	512470.000000	512470.000000	512470.000000	512470.000000	512470.000000	512470.000000
mean	500.118391	8.397934	18.541864	2747.504902	17.765052	7.152272	8.395594	49.442439	2.823803
std	501.419262	0.549133	29.693695	2322.698454	21.789025	11.028943	1.638170	3.466325	4.579425
min	1.000000	5.200000	0.000000	43.000000	0.000000	1.000000	2.500000	41.328376	-0.369758
25%	169.000000	8.100000	2.000000	1161.000000	5.000000	1.000000	7.500000	48.214662	-0.143372
50%	343.000000	8.400000	9.000000	2134.000000	11.000000	3.000000	8.800000	51.499981	0.010607
75%	666.000000	8.800000	23.000000	3633.000000	22.000000	8.000000	9.600000	51.516288	4.834443
max	2682.000000	9.800000	408.000000	16670.000000	395.000000	355.000000	10.000000	52.400181	16.429233

	text	review_type	cleaned_text
0	Being really picky here as all was great but ...	Good_review	be realli picki here a all wa great but a coup...
1	We were given unbeknown to us a handicap acce...	Good_review	we were given unbeknown to u a handicap access...
2	Location a little restrictive Hotel facilities	Good_review	locat a littl restrict hotel facil
3	Staff service at the bar was appalling Bed w...	Good_review	staff servic at the bar wa appal bed wa veri c...
4	No information in rooms about London and some...	Good_review	no inform in room about london and some staff ...

	text	review_type	cleaned_text	review_list
0	Being really picky here as all was great but ...	Good_review	be realli picki here a all wa great but a coup...	[be, realli, picki, here, a, all, wa, great, b...
1	We were given unbeknown to us a handicap acce...	Good_review	we were given unbeknown to u a handicap access...	[we, were, given, unbeknown, to, u, a, handica...
2	Location a little restrictive Hotel facilities	Good_review	locat a littl restrict hotel facil	[locat, a, littl, restrict, hotel, facil]
3	Staff service at the bar was appalling Bed w...	Good_review	staff servic at the bar wa appal bed wa veri c...	[staff, servic, at, the, bar, wa, appal, bed, ...
4	No information in rooms about London and some...	Good_review	no inform in room about london and some staff ...	[no, inform, in, room, about, london, and, som...

Project¶

1. Title:¶

2. Brief on the project:¶

3. About the dataset:¶

4. Objective:¶

5. Individual Details:¶

Importing Libraries¶

Resample Dataset¶

Preprocessing of Text¶

Function to Remove Emojis¶

Function to Convert text to lowercase,remove (/r,/n characters), URLs, non-utf characters, Numbers, punctutations¶

Function to remove contractions¶

Function to Clean Hashtags¶

Function to FIlter Special Characters such as $, &¶

Function to remove multiple sequence spaces¶

Function to apply stemming to words¶

Function to apply lemmatization to words¶

Function to Preprocess the text by applying all above functions¶

Cleaned text added¶

Dealing with Duplicates¶

Duplicates removed¶

Tokenization¶

Removing text without words¶

Feature Engineering¶

TF-IDF¶

Trying Different ML Models¶

Logistic Regression¶

Support Vector Classifier¶

Naive Bayes Classifier¶

Decision Tree Classifier¶

Random Forest Classifier¶

Adaboost Classifier¶

Fine Tuning SVC¶

Model Evaluation¶

Saving the model¶

6. Final Report¶

	Hotel_Address	Additional_Number_of_Scoring	Review_Date	Average_Score	Hotel_Name	Reviewer_Nationality	Negative_Review	Review_Total_Negative_Word_Counts	Total_Number_of_Reviews	Positive_Review	Review_Total_Positive_Word_Counts	Total_Number_of_Reviews_Reviewer_Has_Given	Reviewer_Score	Tags	days_since_review	lat	lng
0	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	8/3/2017	7.7	Hotel Arena	Russia	I am so angry that i made this post available...	397	1403	Only the park outside of the hotel was beauti...	11	7	2.9	[' Leisure trip ', ' Couple ', ' Duplex Double...	0 days	52.360576	4.915968
1	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	8/3/2017	7.7	Hotel Arena	Ireland	No Negative	0	1403	No real complaints the hotel was great great ...	105	7	7.5	[' Leisure trip ', ' Couple ', ' Duplex Double...	0 days	52.360576	4.915968
2	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/31/2017	7.7	Hotel Arena	Australia	Rooms are nice but for elderly a bit difficul...	42	1403	Location was good and staff were ok It is cut...	21	9	7.1	[' Leisure trip ', ' Family with young childre...	3 days	52.360576	4.915968
3	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/31/2017	7.7	Hotel Arena	United Kingdom	My room was dirty and I was afraid to walk ba...	210	1403	Great location in nice surroundings the bar a...	26	1	3.8	[' Leisure trip ', ' Solo traveler ', ' Duplex...	3 days	52.360576	4.915968
4	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/24/2017	7.7	Hotel Arena	New Zealand	You When I booked with your company on line y...	140	1403	Amazing location and building Romantic setting	8	3	6.7	[' Leisure trip ', ' Couple ', ' Suite ', ' St...	10 days	52.360576	4.915968

	count	unique	top	freq
Hotel_Address	512470	1476	163 Marsh Wall Docklands Tower Hamlets London ...	4789
Review_Date	512470	731	8/2/2017	2584
Hotel_Name	512470	1475	Britannia International Hotel Canary Wharf	4789
Reviewer_Nationality	512470	227	United Kingdom	244457
Negative_Review	512470	327927	No Negative	127035
Positive_Review	512470	409941	No Positive	35737
Tags	512470	54934	[' Leisure trip ', ' Couple ', ' Double Room '...	5100
days_since_review	512470	731	1 days	2584

	Total_Review	review_type
0	I am so angry that i made this post available...	Bad_review
1	No real complaints the hotel was great great ...	Good_review
2	Rooms are nice but for elderly a bit difficul...	Good_review
3	My room was dirty and I was afraid to walk ba...	Bad_review
4	You When I booked with your company on line y...	Bad_review
...	...	...
515733	no trolly or staff to help you take the lugga...	Good_review
515734	The hotel looks like 3 but surely not 4 Brea...	Bad_review
515735	The ac was useless It was a hot week in vienn...	Bad_review
515736	The rooms are enormous and really comfortable...	Good_review
515737	I was in 3rd floor It didn t work Free Wife ...	Good_review